Robust feature extraction using subband spectral centroid histograms

Abstract

In this paper we propose a new framework for utilizing frequency information from the short-term power spectrum of speech. Feature extraction is based on the cepstral coefficients derived from the histograms of subband spectral centroids (SSC). Two new feature extraction algorithms are proposed, one based on frequency information alone, and the other which efficiently combines the frequency and amplitude information from the speech power spectrum. Experimental study on an automatic speech recognition task shows that the proposed methods outperform the conventional speech front-ends in the presence of additive white noise, while they perform comparably in the noise-free conditions.